#Load packages
pacman::p_load(plotly, ggstatsplot, knitr, tidyverse)
#Import data
flatprice <- read_csv("data/resale-flat-prices-based-on-registration-date-from-jan-2017-onwards.csv", show_col_types = FALSE)Take-home Exercise 3
Visual Analytics of Resale Prices of Singapore Public Housing Properties
1. Overview
This exercise aims to uncover the salient patterns of the resale prices of public housing property by residential towns and estates in Singapore using appropriate analytical visualisation techniques. The visualization is designed using ggplot2, its extensions, and tidyverse packages.
The original dataset was downloaded from Data.gov.sg titled Resale flat princes based on registration date from Jan-2017 onwards.
The file downloaded was resale-flat-prices-based-on-registration-date-from-jan-2017-onwards.csv
The focus of the study is on 3-ROOM, 4-ROOM and 5-ROOM types for 2022 period.
2. Data Preparation
2.1 Install R packages and import dataset
The code chunk below uses pacman::p_load() to check if packages are installed. If they are, they will be launched into R. The packages installed are
plotly: Used for creating interactive web-based graphs.ggstatsplot: Used for creating graphics with details from statistical tests.knitr: Used for dynamic report generationtidyverse: A collection of core packages designed for data science, used extensively for data preparation and wrangling.All packages can be found within CRAN.
Import data from csv using readr::read_csv() and store it in variable flatprice.
2.2 Data wrangling
Looking at the data below, we notice few problems
month is in
<chr>format (“yyyy-mm”), which is not very useful for filtering for 2022 periodlease_commence_date is in
<dbl>format. It needs to be converted to<int>remaining_lease is in
<chr>format. It needs to be reformatted to<dbl>in years
flatprice# A tibble: 146,215 × 11
month town flat_…¹ block stree…² store…³ floor…⁴ flat_…⁵ lease…⁶ remai…⁷
<chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
1 2017-01 ANG MO… 2 ROOM 406 ANG MO… 10 TO … 44 Improv… 1979 61 yea…
2 2017-01 ANG MO… 3 ROOM 108 ANG MO… 01 TO … 67 New Ge… 1978 60 yea…
3 2017-01 ANG MO… 3 ROOM 602 ANG MO… 01 TO … 67 New Ge… 1980 62 yea…
4 2017-01 ANG MO… 3 ROOM 465 ANG MO… 04 TO … 68 New Ge… 1980 62 yea…
5 2017-01 ANG MO… 3 ROOM 601 ANG MO… 01 TO … 67 New Ge… 1980 62 yea…
6 2017-01 ANG MO… 3 ROOM 150 ANG MO… 01 TO … 68 New Ge… 1981 63 yea…
7 2017-01 ANG MO… 3 ROOM 447 ANG MO… 04 TO … 68 New Ge… 1979 61 yea…
8 2017-01 ANG MO… 3 ROOM 218 ANG MO… 04 TO … 67 New Ge… 1976 58 yea…
9 2017-01 ANG MO… 3 ROOM 447 ANG MO… 04 TO … 68 New Ge… 1979 61 yea…
10 2017-01 ANG MO… 3 ROOM 571 ANG MO… 01 TO … 67 New Ge… 1979 61 yea…
# … with 146,205 more rows, 1 more variable: resale_price <dbl>, and
# abbreviated variable names ¹flat_type, ²street_name, ³storey_range,
# ⁴floor_area_sqm, ⁵flat_model, ⁶lease_commence_date, ⁷remaining_leaseis.na() function is also used to confirm that there are no missing values in the flatprice dataset.
#Check for missing values
any(is.na(flatprice))[1] FALSEThe code chunk below performs the required data wrangling to clean flatprice dataset and store it in new variable flatpriceclean.
- Filter flat_type for 3 ROOM, 4 ROOM, and 5 ROOM as this is the scope of the study using
dplyr::filter() - Convert the month variable to date using
as.Date(). Store the year and month to the respective new variables year and month usingdplyr:mutate(). They can then be converted to integer usingas.integer(). Afterwards, we can filter the year variable to 2022 usingdplyr::filter(), which is the scope of the study - Extract the year and month digits from remaining_lease variable using
str_extract()function. Sum the year digit and (month digit/12) to obtain the years of remaining lease and convert it to<dbl>format usingas.numeric(). They are then rounded to 1 decimal place usinground(). Assign new variable called remaining_lease_years usingdplyr::mutate() - Create new variable called resale_price_persqm to divide the resale_price with floor_area_sqm. This is performed to normalize the resale price to flat area. They are then rounded to 1 decimal place using
round().The new variable is assigned usingdplyr::mutate - Convert lease_commence_date to
<int>usingas.integer()
#Data preparation
#store the new dataset in new variable flatpriceclean
flatpriceclean <- flatprice |>
#filter for 3-ROOM, 4-ROOM, 5-ROOM
filter(flat_type %in% c('3 ROOM','4 ROOM','5 ROOM')) |>
#reformat month and split it to month and year. Use year to filter for 2022
mutate(year = as.integer(format(as.Date(paste(month, "-01", sep="")), "%Y")),
month = as.integer(format(as.Date(paste(month, "-01", sep="")), "%m")),
.before = 1)|>
filter(year == 2022) |>
#mutate remaining_lease to remaining_lease_years
mutate(remaining_lease_years = round((as.numeric(str_extract(remaining_lease, "^[0-9]+")) +
ifelse(is.na(as.numeric(str_extract(remaining_lease, " [0-9]+"))), 0, as.numeric(str_extract(remaining_lease, " [0-9]+")))/12), digits = 1),
#create new variable called resale_price_persqm
resale_price_persqm = round(resale_price/floor_area_sqm, digits = 1),
#convert lease_commence_date to integer
lease_commence_date = as.integer(lease_commence_date),
.after = remaining_lease) The final dataset flatpriceclean is displayed below.
kable(head(flatpriceclean), "simple")| year | month t | own f | lat_type b | lock s | treet_name s | torey_range | floor_area_sqm f | lat_model | lease_commence_date r | emaining_lease | remaining_lease_years | resale_price_persqm | resale_price |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022 | 1 | ANG MO KIO | 3 ROOM | 320 | ANG MO KIO AVE 1 | 07 TO 09 | 73 | New Generation | 1977 | 54 years 05 months | 54.4 | 4904.1 | 358000 |
| 2022 | 1 | ANG MO KIO | 3 ROOM | 225 | ANG MO KIO AVE 1 | 07 TO 09 | 67 | New Generation | 1978 | 55 years 01 month | 55.1 | 5298.5 | 355000 |
| 2022 | 1 | ANG MO KIO | 3 ROOM | 331 | ANG MO KIO AVE 1 | 07 TO 09 | 68 | New Generation | 1981 | 58 years | 58.0 | 4970.6 | 338000 |
| 2022 | 1 | ANG MO KIO | 3 ROOM | 534 | ANG MO KIO AVE 10 | 07 TO 09 | 82 | New Generation | 1980 | 57 years 02 months | 57.2 | 5122.0 | 420000 |
| 2022 | 1 | ANG MO KIO | 3 ROOM | 578 | ANG MO KIO AVE 10 | 04 TO 06 | 67 | New Generation | 1980 | 57 years 01 month | 57.1 | 4895.5 | 328000 |
| 2022 | 1 | ANG MO KIO | 3 ROOM | 452 | ANG MO KIO AVE 10 | 01 TO 03 | 83 | New Generation | 1979 | 56 years 07 months | 56.6 | 4337.3 | 360000 |
3. Visualisation
3.1 Exploratory Data Visualisation
The plots here are preliminary in nature but designed with interactivity to allow users to perform Exploratory Data Analysis (EDA) Visualisation to study the data.
3.1.1. Interactive scatterplot
Design Consideration
Scatterplots are generally used to discover relationship between two continuous variables. As such, the visualization below allows users to select the x-axis and y-axis of the continuous variables they wish to study. Considerations :
y-axis selection is resale_price and resale_price_persqm. This is aligned with the study purpose of discovering patterns of resale price. This allows users to also see the intent of normalising resale_price by floor_area_sqm
x-axis selection is other continuous variables, namely: remaining_lease_years, lease_commence_date, and floor_area_sqm
As the study also aims to focus on the flat type, the scatterplot is colored by flat_type
As the plots are expected to be very scattered, opacity is introduced with white border
Preparation of visualisation
plot_ly is used to prepare the interactive plot. Steps taken are
Initiating base scatterplot, indicated by
type = 'scatter', colored by flat_type.markerargument is used to introduceopacityandline(white plot border)layoutargument is used to add plot title, x-axis title, and y-axis titleTo create the dropdown menu for parameters of x-axis and y-axis, the
updatemenusargument is used to create respectivebuttons
Refer to code below for more details
Show the code
#Initiating the base plot
plot_ly(data = flatpriceclean,
x = ~remaining_lease_years,
y = ~resale_price_persqm,
type = 'scatter',
mode = 'markers',
color = ~flat_type,
marker = list(opacity = 0.6,
sizemode = 'diameter',
line = list(width = 0.2, color = 'white'))) |>
#Generating plot, x-axis, and y-axis title
layout(title = "Interactive scatterplot of resale price vs other factors\nResale transactions, 2022",
xaxis = list(title = "Remaining Lease (Year)"),
yaxis = list(title = "Resale Price per sqm (SGD)"),
#creating dropwdown menus to allow selection of parameters on x-axis and y-axis
updatemenus = list(list(type = "dropdown",
direction = "up",
xref = "paper",
yref = "paper",
xanchor = "left",
yanchor = "top",
x = 1,
y = 0,
buttons = list(
list(method = "update",
args = list(list(x = list(flatpriceclean$remaining_lease_years)),
list(xaxis = list(title = "Remaining Lease (Year)"))),
label = "Remaining Lease"),
list(method = "update",
args = list(list(x = list(flatpriceclean$lease_commence_date)),
list(xaxis = list(title = "Year of Lease Commenced"))),
label = "Lease Commenced"),
list(method = "update",
args = list(list(x = list(flatpriceclean$floor_area_sqm)),
list(xaxis = list(title = "Floor Area (sqm)"))),
label = "Floor Area")
)
),
list(type = "dropdown",
xref = "paper",
yref = "paper",
xanchor = "left",
yanchor = "top",
x = 0.04,
y = 0.95,
buttons = list(
list(method = "update",
args = list(list(y = list(flatpriceclean$resale_price_persqm)),
list(yaxis = list(title = "Resale Price per sqm (SGD)"))),
label = "Resale Price/Area"),
list(method = "update",
args = list(list(y = list(flatpriceclean$resale_price)),
list(yaxis = list(title = "Resale Price (SGD)"))),
label = "Resale Price")
)
)
)
)ggplot(data = flatpriceclean)+
geom_point(aes(x = lease_commence_date,
y = remaining_lease_years)) +
labs(
x = "Year of Lease Commenced",
y = "Remaining Lease\n(Year)") +
theme(axis.title.y = element_text(angle = 0))
The first plot purpose is to provide preliminary insight on the resale price of property vs remaining lease. the plot looks very cluttered as the number of dataset is high, however, this is deemed to be sufficient for preliminary analysis. Note that the resale price is normalized with floor area, as absolute resale price tends to be more expensive for bigger area.
The first plot design consideration :
Color legend for flat type (3 ROOM, 4 ROOM, 5 ROOM) in plotly allows users to filter accordingly
Hover tip displaying the resale price, floor area, remaining lease, and flat type
Show the code
#plot_ly(data = flatpriceclean,
# x = ~remaining_lease_years,
# y = ~resale_price_persqm,
# hovertemplate = ~paste("<br>Resale Price per sqm:", resale_price_persqm,
# "<br>Floor Area (sqm):", floor_area_sqm,
# "<br>Remaining Lease (Year):", remaining_lease_years),
# type = 'scatter',
# mode = 'markers',
# color = ~flat_type,
# marker = list(opacity = 0.6,
# sizemode = 'diameter',
# line = list(width = 0.2, color = '#FFFFFF'))) |>
# layout(title = "Resale Price per flat area increases with remaining lease \nResale transactions, 2022",
# xaxis = list(title = "Remaining Lease (Year)"),
# yaxis = list(title = "Resale Price per sqm (SGD)"),
# legend = list(orientation = 'h',
# xref = "paper",
# yref = "paper",
# xanchor = "center",
# yanchor ="top",
# x = 0.5,
# y = 0.95))Using updatemenus to get a good first glance of all relationships
Show the code
plot_ly(data = flatpriceclean,
x = ~flat_type,
y = ~resale_price_persqm,
type = "violin",
alpha = 0.3,
marker = list(opacity = 0.6),
box = list(visible = T),
meanline = list(visible = T)) |>
layout(title = "Distribution of resale price by selected factors, \nResale transactions, 2022",
xaxis = list(title = ""),
yaxis = list(title = "Resale Price per sqm (SGD)"),
updatemenus = list(list(type = 'dropdown',
xref = "paper",
yref = "paper",
xanchor = "left",
x = 0.04,
y = 0.95,
buttons = list(
list(method = "update",
args = list(list(x = list(flatpriceclean$flat_type)),
list(xaxis = list(categoryorder = "category ascending"))),
label = "Flat Type"),
list(method = "update",
args = list(list(x = list(flatpriceclean$flat_model)),
list(xaxis = list(categoryorder = "mean ascending"))),
label = "Flat Model"),
list(method = "update",
args = list(list(x = list(flatpriceclean$storey_range)),
list(xaxis = list(categoryorder = "category ascending"))),
label = "Storey Height"),
list(method = "update",
args = list(list(x = list(flatpriceclean$town)),
list(xaxis = list(categoryorder = "mean ascending"))),
label = "Town"),
list(method = "update",
args = list(list(x = list(flatpriceclean$month)),
list(xaxis = list(tickmode = "array")),
list(color = list(flatpriceclean$month))),
label = "Transaction Month")
)
)
)
)3.2 Confirmatory Data Analysis Visualization
The first plot is to investigate other factors that might impact the resale price.
Show the code
ggbetweenstats(
data = flatpriceclean,
x = flat_type,
y = resale_price_persqm,
xlab = "Types of Flat (Rooms)",
ylab = "Resale Price per sqm (SGD)",
palette = "Paired",
title = "One-way ANOVA analysis reveals at least one significant difference in 2022 resale price across different flat types",
type = "np",
pairwise.comparisons = TRUE,
pairwise.display = "ns",
mean.ci = TRUE,
p.adjust.method = "fdr",
messages = FALSE
) 
The second plot is to investigate other factors that might impact the resale price.
Show the code
ggbetweenstats(
data = flatpriceclean |>
mutate(storey_range = ifelse(storey_range %in% c("40 TO 42", "43 TO 45", "46 TO 48", "49 TO 51"), "40+", storey_range)),
x = storey_range,
y = resale_price_persqm,
xlab = "Storey Height",
ylab = "Resale Price per sqm (SGD)",
palette = "Paired",
title = "One-way ANOVA analysis reveals at least one significant difference in 2022 resale price across different storeys",
type = "np",
pairwise.comparisons = TRUE,
pairwise.display = "ns",
mean.ci = TRUE,
p.adjust.method = "fdr",
messages = FALSE
) 
Thirdly, check the flat_model variables. Filtering for number of observations >= 50
Show the code
flatpriceclean$flat_model <- fct_reorder(flatpriceclean$flat_model, flatpriceclean$resale_price_persqm)
ggbetweenstats(
data = flatpriceclean |>
group_by(flat_model) |>
filter(n() >= 50),
x = flat_model,
y = resale_price_persqm,
xlab = "Flat Model",
ylab = "Resale Price per sqm (SGD)",
palette = "Paired",
title = "One-way ANOVA analysis reveals at least one significant difference in 2022 resale price across different models",
type = "np",
pairwise.comparisons = TRUE,
pairwise.display = "ns",
mean.ci = TRUE,
p.adjust.method = "fdr",
messages = FALSE
) 
The town variables are skipped as there are too many variables -> to be considered in the final visualization
Lastly, check the transaction month variables
Show the code
ggbetweenstats(
data = flatpriceclean,
x = month,
y = resale_price_persqm,
xlab = "Month of Transaction",
ylab = "Resale Price per sqm (SGD)",
palette = "Paired",
title = "One-way ANOVA analysis reveals at least one significant difference in 2022 resale price across different \ntransaction months",
type = "np",
pairwise.comparisons = TRUE,
pairwise.display = "ns",
mean.ci = TRUE,
p.adjust.method = "fdr",
messages = FALSE
) 
3.3 Visualization of Resale Price by Township
Show the code
town_list <- list()
for (i in 1:length(unique(flatpriceclean$town))) {
town_list[[i]] <- list(method = "restyle",
args = list("transforms[0].value",
unique(flatpriceclean$town)[i]),
label = unique(flatpriceclean$town)[i])
}
annot <- list(list(text = "Select Towns:",
x = 1.41,
y = 0.78,
xref = 'paper',
yref = 'paper',
showarrow = FALSE))Show the code
flatpriceorder <- flatpriceclean[order(flatpriceclean$flat_type), ]
plot_ly(data = flatpriceclean,
x = ~remaining_lease_years,
y = ~resale_price_persqm,
hovertemplate = ~paste("<br>Resale Price per sqm:", resale_price_persqm,
"<br>Floor Area (sqm):", floor_area_sqm,
"<br>Remaining Lease (Year):", remaining_lease_years,
"<br>Town:", town),
type = 'scatter',
mode = 'markers',
size = ~floor_area_sqm,
sizes = c(5, 15),
color = ~factor(flat_type),
marker = list(opacity = 0.6,
sizemode = 'diameter',
line = list(width = 0.2, color = '#FFFFFF')),
transforms = list(list(type = 'filter',
target = ~flatpriceorder$town,
operation = '=',
value = unique(flatpriceorder$town)[1])
)
) |>
layout(title = "Resale Price per flat area increases with remaining lease \nResale transactions by towns, 2022",
xaxis = list(title = "Remaining Lease (Year)",
range = c(40, 100)),
yaxis = list(title = "Resale Price per sqm (SGD)",
range = c(3000, 16000)),
updatemenus = list(list(type = 'dropdown',
xref = "paper",
yref = "paper",
x = 1.4, y = 0.7,
buttons = town_list)
),
annotations = annot
)